In　order　to　utilize　the　frequency　domain　information　of　motor　imagery　electroencephalogram(MI-EEG)　signals　to　effectively　and　accurately　reflect　the　nonlinear　causal　interaction　between　different　EEG　electrodes,　this　paper　presents　a　brain　functional　network　based　on　continuous　wavelet　transform　and　symbolic　transfer　entropy.　Firstly,　the　continuous　wavelet　transform　is　applied　to　each　MI-EEG　signal　to　compute　the　time-frequency-energy　matrix.　Then,　the　one-dimensional　time-frequency　energy　sequence　of　each　channel　is　obtained　by　joining　serially　spliced　time-energy　sequence　in　the　frequency　band　closely　related　to　motor　imagery.　Finally,　the　brain　connectivity　matrix　is　calculated　based　on　the　symbolic　transfer　entropy　between　the　time-frequency　energy　sequences　of　any　two　channels,　and　the　brain　functional　network　is　constructed.The　experiment　results　show　that　the　brain　functional　network　constructed　with　the　symbolic　transfer　entropy　between　time-frequency　energy　sequences　can　effectively　reflect　the　time-frequency　characteristics　and　nonlinear　characteristic　information　transmission　of　MI-EEG.　Compared　with　the　traditional　brain　network　construction　method,　it　is　beneficial　to　enhance　the　separability　of　different　motor　imagery　tasks.　©　2022　Chinese　Institute　of　Electronics.　All　rights　reserved.